Advanced Sentiment Analysis of IMDB Movie Reviews Using NLP Preprocessing, TF-IDF and Classical Machine Learning Models

Abstract

Sentiment analysis (or opinion) is a core problem in Natural Language Processing (NLP) that aims to automatically determine the polarity of opinions expressed in text. As online review platforms continue to expand, the volume of user generated content has grown beyond what can be examined manually. Movie reviews are particularly challenging because they are subjective, vary widely in length, and use diverse writing styles. This paper presents a sentiment classification pipeline for IMDb movie reviews that combine rigorous text cleaning with Term Frequency Inverse Document Frequency (TF–IDF) feature extraction and classical supervised learning models. Exploratory analysis is used to examine sentiment balance and review-length characteristics. The pre-processing stage removes HTML/URLs, normalizes text, filters stop words, and applies lemmatization to reduce noise and vocabulary sparsity. TF–IDF vectors with unigram and bigram features are then constructed to capture both individual sentiment cues and short contextual phrases. Logistic Regression, Linear SVM, Multinomial Naïve Bayes, and Random Forest are evaluated under a consistent experimental setup. Performance is reported using accuracy, precision, recall, F1-score, and ROC-based analysis. The results show that Logistic Regression achieves the best overall performance, with an accuracy of 88.68% and an F1-score of 88.62%, followed closely by Linear SVM. Overall, TF–IDF with linear classifiers remains an interpretable and computationally efficient baseline for practical sentiment classification.